Terminal Times

Get the times the first and last observations were taken.

Usage

# S4 method for class 'TimeSeries'
start(x, calendar = NULL, ...)

# S4 method for class 'TimeIntervals'
start(x, calendar = NULL, ...)

# S4 method for class 'TimeSeries'
end(x, calendar = NULL, ...)

# S4 method for class 'TimeIntervals'
end(x, calendar = NULL, ...)

Arguments

x

A TimeSeries object.

calendar

A TimeScale object specifying the target calendar (see calendar()). If NULL (the default), rata die are returned.

...

Currently not used.

Value

A numeric vector of decimal years (if calendar is not NULL).

See also

Other tools: overlap(), span(), time(), window()

Author

N. Frerebeau

Examples

## Create time-series of 20 observations

## Univariate
## Sampled every years starting from 1029 BCE
(X <- series(rnorm(30), time = 1029:1000, calendar = BCE()))
#> 30 x 1 x 1 time series observed between -376199 and -365607 r.d.

## Terminal and sampling times (returns rata die)
start(X)
#> [1] -376199
end(X)
#> [1] -365607
time(X)
#> Rata die: number of days since 01-01-01 (Gregorian).
#>  [1] -376199 -375834 -375468 -375103 -374738 -374373 -374007 -373642 -373277
#> [10] -372912 -372546 -372181 -371816 -371451 -371085 -370720 -370355 -369990
#> [19] -369624 -369259 -368894 -368529 -368163 -367798 -367433 -367068 -366702
#> [28] -366337 -365972 -365607
span(X)
#> [1] 10592

## Multivariate
## Sampled every century starting from 1000 CE
(Y <- series(matrix(rnorm(90), 30, 3), time = 1000:1029, calendar = CE()))
#> 30 x 3 x 1 time series observed between 364878 and 375470 r.d.

## Terminal and sampling times (returns Gregorian Common Era years)
start(Y, calendar = CE())
#> [1] 1000
end(Y, calendar = CE())
#> [1] 1029
time(Y, calendar = CE())
#>  [1] 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014
#> [16] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029
span(Y, calendar = CE())
#> [1] 29

## Coerce to data frame
df <- as.data.frame(Y, calendar = BP())
head(df)
#>   time series variable      value
#> 1  950     S1        A  0.5196726
#> 2  949     S1        A -2.4229113
#> 3  948     S1        A -0.8062903
#> 4  947     S1        A -0.4846800
#> 5  946     S1        A -0.6530773
#> 6  945     S1        A -0.2937642